Overview

Dataset statistics

Number of variables19
Number of observations3,816
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory540.5 KiB
Average record size in memory145.0 B

Variable types

Numeric15
Categorical3
Boolean1

Warnings

oid is uniformly distributed Uniform
Revenue is uniformly distributed Uniform
oid has unique values Unique
Administrative has 1597 (41.9%) zeros Zeros
Administrative_Duration has 1629 (42.7%) zeros Zeros
Informational has 2925 (76.7%) zeros Zeros
Informational_Duration has 2980 (78.1%) zeros Zeros
ProductRelated_Duration has 164 (4.3%) zeros Zeros
BounceRates has 2074 (54.4%) zeros Zeros
PageValues has 2171 (56.9%) zeros Zeros
SpecialDay has 3655 (95.8%) zeros Zeros

Reproduction

Analysis started2021-01-15 09:27:03.398455
Analysis finished2021-01-15 09:27:44.079617
Duration40.68 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

oid
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct3816
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1908.5
Minimum1
Maximum3816
Zeros0
Zeros (%)0.0%
Memory size29.9 KiB
2021-01-15T14:57:44.314111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile191.75
Q1954.75
median1908.5
Q32862.25
95-th percentile3625.25
Maximum3816
Range3815
Interquartile range (IQR)1907.5

Descriptive statistics

Standard deviation1101.728642
Coefficient of variation (CV)0.5772746354
Kurtosis-1.2
Mean1908.5
Median Absolute Deviation (MAD)954
Skewness0
Sum7282836
Variance1213806
MonotocityStrictly increasing
2021-01-15T14:57:44.454399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
12821
 
< 0.1%
12581
 
< 0.1%
33071
 
< 0.1%
12621
 
< 0.1%
33111
 
< 0.1%
12661
 
< 0.1%
33151
 
< 0.1%
12701
 
< 0.1%
33191
 
< 0.1%
Other values (3806)3806
99.7%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
ValueCountFrequency (%)
38161
< 0.1%
38151
< 0.1%
38141
< 0.1%
38131
< 0.1%
38121
< 0.1%

Administrative
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.491614256
Minimum0
Maximum26
Zeros1597
Zeros (%)41.9%
Memory size29.9 KiB
2021-01-15T14:57:44.571410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum26
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.342005255
Coefficient of variation (CV)1.341301226
Kurtosis4.08288512
Mean2.491614256
Median Absolute Deviation (MAD)1
Skewness1.833646261
Sum9508
Variance11.16899912
MonotocityNot monotonic
2021-01-15T14:57:44.687790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
01597
41.9%
1468
 
12.3%
2384
 
10.1%
3313
 
8.2%
4253
 
6.6%
5191
 
5.0%
6155
 
4.1%
799
 
2.6%
897
 
2.5%
981
 
2.1%
Other values (14)178
 
4.7%
ValueCountFrequency (%)
01597
41.9%
1468
 
12.3%
2384
 
10.1%
3313
 
8.2%
4253
 
6.6%
ValueCountFrequency (%)
261
< 0.1%
241
< 0.1%
222
0.1%
201
< 0.1%
191
< 0.1%

Administrative_Duration
Real number (ℝ)

ZEROS

Distinct1368
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.6292458
Minimum-1
Maximum2086.75
Zeros1629
Zeros (%)42.7%
Memory size29.9 KiB
2021-01-15T14:57:44.820521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median18
Q3100.4541667
95-th percentile390.7
Maximum2086.75
Range2087.75
Interquartile range (IQR)100.4541667

Descriptive statistics

Standard deviation182.5820165
Coefficient of variation (CV)2.037080809
Kurtosis27.79732972
Mean89.6292458
Median Absolute Deviation (MAD)18
Skewness4.477995069
Sum342025.202
Variance33336.19275
MonotocityNot monotonic
2021-01-15T14:57:44.945350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01629
42.7%
-123
 
0.6%
718
 
0.5%
918
 
0.5%
417
 
0.4%
1416
 
0.4%
1514
 
0.4%
614
 
0.4%
2112
 
0.3%
1112
 
0.3%
Other values (1358)2043
53.5%
ValueCountFrequency (%)
-123
 
0.6%
01629
42.7%
1.3333333331
 
< 0.1%
23
 
0.1%
312
 
0.3%
ValueCountFrequency (%)
2086.751
< 0.1%
2047.2348481
< 0.1%
1668.51
< 0.1%
1660.31
< 0.1%
1640.5909091
< 0.1%

Informational
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5545073375
Minimum0
Maximum16
Zeros2925
Zeros (%)76.7%
Memory size29.9 KiB
2021-01-15T14:57:45.049546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.328650166
Coefficient of variation (CV)2.396091226
Kurtosis19.08982013
Mean0.5545073375
Median Absolute Deviation (MAD)0
Skewness3.640271619
Sum2116
Variance1.765311264
MonotocityNot monotonic
2021-01-15T14:57:45.172413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
02925
76.7%
1370
 
9.7%
2213
 
5.6%
3135
 
3.5%
479
 
2.1%
541
 
1.1%
628
 
0.7%
78
 
0.2%
96
 
0.2%
84
 
0.1%
Other values (4)7
 
0.2%
ValueCountFrequency (%)
02925
76.7%
1370
 
9.7%
2213
 
5.6%
3135
 
3.5%
479
 
2.1%
ValueCountFrequency (%)
161
 
< 0.1%
141
 
< 0.1%
122
 
0.1%
103
0.1%
96
0.2%

Informational_Duration
Real number (ℝ)

ZEROS

Distinct552
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.23282744
Minimum-1
Maximum1767.666667
Zeros2980
Zeros (%)78.1%
Memory size29.9 KiB
2021-01-15T14:57:45.351428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile243
Maximum1767.666667
Range1768.666667
Interquartile range (IQR)0

Descriptive statistics

Standard deviation143.7699803
Coefficient of variation (CV)3.573449579
Kurtosis44.88943584
Mean40.23282744
Median Absolute Deviation (MAD)0
Skewness5.991777064
Sum153528.4695
Variance20669.80722
MonotocityNot monotonic
2021-01-15T14:57:45.532946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02980
78.1%
-123
 
0.6%
916
 
0.4%
1611
 
0.3%
78
 
0.2%
267
 
0.2%
67
 
0.2%
187
 
0.2%
416
 
0.2%
36
 
0.2%
Other values (542)745
 
19.5%
ValueCountFrequency (%)
-123
 
0.6%
02980
78.1%
11
 
< 0.1%
25
 
0.1%
36
 
0.2%
ValueCountFrequency (%)
1767.6666671
< 0.1%
1665.0666671
< 0.1%
16521
< 0.1%
16361
< 0.1%
14881
< 0.1%

ProductRelated
Real number (ℝ≥0)

Distinct229
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.04114256
Minimum0
Maximum534
Zeros9
Zeros (%)0.2%
Memory size29.9 KiB
2021-01-15T14:57:45.708887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median19
Q338
95-th percentile116
Maximum534
Range534
Interquartile range (IQR)30

Descriptive statistics

Standard deviation46.63069976
Coefficient of variation (CV)1.411291988
Kurtosis24.45528183
Mean33.04114256
Median Absolute Deviation (MAD)13
Skewness4.067289403
Sum126085
Variance2174.42216
MonotocityNot monotonic
2021-01-15T14:57:45.892753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1168
 
4.4%
2134
 
3.5%
3134
 
3.5%
7123
 
3.2%
8122
 
3.2%
6112
 
2.9%
5106
 
2.8%
12104
 
2.7%
10102
 
2.7%
1399
 
2.6%
Other values (219)2612
68.4%
ValueCountFrequency (%)
09
 
0.2%
1168
4.4%
2134
3.5%
3134
3.5%
499
2.6%
ValueCountFrequency (%)
5341
< 0.1%
5171
< 0.1%
5011
< 0.1%
4701
< 0.1%
4391
< 0.1%

ProductRelated_Duration
Real number (ℝ)

ZEROS

Distinct3276
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1297.951396
Minimum-1
Maximum27009.85943
Zeros164
Zeros (%)4.3%
Memory size29.9 KiB
2021-01-15T14:57:46.072371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile4.75
Q1242.7083334
median686.3611111
Q31524.972248
95-th percentile4816.25723
Maximum27009.85943
Range27010.85943
Interquartile range (IQR)1282.263914

Descriptive statistics

Standard deviation1888.82047
Coefficient of variation (CV)1.455232049
Kurtosis25.41468162
Mean1297.951396
Median Absolute Deviation (MAD)543.3833335
Skewness3.943345199
Sum4952982.528
Variance3567642.767
MonotocityNot monotonic
2021-01-15T14:57:46.261030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0164
 
4.3%
-123
 
0.6%
227
 
0.2%
87
 
0.2%
366
 
0.2%
136
 
0.2%
56
 
0.2%
196
 
0.2%
506
 
0.2%
595
 
0.1%
Other values (3266)3580
93.8%
ValueCountFrequency (%)
-123
 
0.6%
0164
4.3%
11
 
< 0.1%
2.6666666671
 
< 0.1%
42
 
0.1%
ValueCountFrequency (%)
27009.859431
< 0.1%
21672.244251
< 0.1%
18504.126211
< 0.1%
17550.584861
< 0.1%
14568.162011
< 0.1%

BounceRates
Real number (ℝ≥0)

ZEROS

Distinct668
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01561189347
Minimum0
Maximum0.2
Zeros2074
Zeros (%)54.4%
Memory size29.9 KiB
2021-01-15T14:57:46.444059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.009090909
95-th percentile0.1
Maximum0.2
Range0.2
Interquartile range (IQR)0.009090909

Descriptive statistics

Standard deviation0.04259113285
Coefficient of variation (CV)2.728120899
Kurtosis12.94058962
Mean0.01561189347
Median Absolute Deviation (MAD)0
Skewness3.717050805
Sum59.57498548
Variance0.001814004598
MonotocityNot monotonic
2021-01-15T14:57:46.631140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02074
54.4%
0.2169
 
4.4%
0.02857142938
 
1.0%
0.01428571428
 
0.7%
0.01666666726
 
0.7%
0.0526
 
0.7%
0.02525
 
0.7%
0.0225
 
0.7%
0.03333333324
 
0.6%
0.122
 
0.6%
Other values (658)1359
35.6%
ValueCountFrequency (%)
02074
54.4%
3.94 × 1051
 
< 0.1%
7.27 × 1051
 
< 0.1%
7.5 × 1051
 
< 0.1%
8.01 × 1051
 
< 0.1%
ValueCountFrequency (%)
0.2169
4.4%
0.1619047621
 
< 0.1%
0.1555555561
 
< 0.1%
0.152
 
0.1%
0.1466666671
 
< 0.1%

ExitRates
Real number (ℝ≥0)

Distinct1661
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03487215642
Minimum0
Maximum0.2
Zeros34
Zeros (%)0.9%
Memory size29.9 KiB
2021-01-15T14:57:46.815754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003636364
Q10.011111111
median0.02
Q30.0375
95-th percentile0.15
Maximum0.2
Range0.2
Interquartile range (IQR)0.026388889

Descriptive statistics

Standard deviation0.04378809673
Coefficient of variation (CV)1.255675049
Kurtosis6.894093217
Mean0.03487215642
Median Absolute Deviation (MAD)0.010909091
Skewness2.662592886
Sum133.0721489
Variance0.001917397415
MonotocityNot monotonic
2021-01-15T14:57:47.000662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2171
 
4.5%
0.06666666793
 
2.4%
0.03333333391
 
2.4%
0.0591
 
2.4%
0.190
 
2.4%
0.02582
 
2.1%
0.01666666772
 
1.9%
0.0466
 
1.7%
0.02857142965
 
1.7%
0.0263
 
1.7%
Other values (1651)2932
76.8%
ValueCountFrequency (%)
034
0.9%
0.0001755931
 
< 0.1%
0.0004807691
 
< 0.1%
0.000586512
 
0.1%
0.0005899711
 
< 0.1%
ValueCountFrequency (%)
0.2171
4.5%
0.1866666671
 
< 0.1%
0.1777777781
 
< 0.1%
0.1666666675
 
0.1%
0.16251
 
< 0.1%

PageValues
Real number (ℝ≥0)

ZEROS

Distinct1625
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.13371569
Minimum0
Maximum361.7637419
Zeros2171
Zeros (%)56.9%
Memory size29.9 KiB
2021-01-15T14:57:47.182549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317.84507539
95-th percentile63.70765161
Maximum361.7637419
Range361.7637419
Interquartile range (IQR)17.84507539

Descriptive statistics

Standard deviation28.40342477
Coefficient of variation (CV)2.009621914
Kurtosis28.38600624
Mean14.13371569
Median Absolute Deviation (MAD)0
Skewness4.156848211
Sum53934.25907
Variance806.7545388
MonotocityNot monotonic
2021-01-15T14:57:47.363117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02171
56.9%
53.9885
 
0.1%
44.893459372
 
0.1%
9.08476782
 
0.1%
59.9882
 
0.1%
12.558857142
 
0.1%
22.7382
 
0.1%
78.569598642
 
0.1%
40.40144812
 
0.1%
16.15855822
 
0.1%
Other values (1615)1624
42.6%
ValueCountFrequency (%)
02171
56.9%
0.0670495461
 
< 0.1%
0.0935469491
 
< 0.1%
0.0986214031
 
< 0.1%
0.1206999141
 
< 0.1%
ValueCountFrequency (%)
361.76374191
< 0.1%
360.95338391
< 0.1%
287.95379281
< 0.1%
270.78469311
< 0.1%
261.49128571
< 0.1%

SpecialDay
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02353249476
Minimum0
Maximum1
Zeros3655
Zeros (%)95.8%
Memory size29.9 KiB
2021-01-15T14:57:47.518086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1224549902
Coefficient of variation (CV)5.203655264
Kurtosis32.57461203
Mean0.02353249476
Median Absolute Deviation (MAD)0
Skewness5.639811435
Sum89.8
Variance0.01499522463
MonotocityNot monotonic
2021-01-15T14:57:47.665646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
03655
95.8%
0.652
 
1.4%
0.435
 
0.9%
0.831
 
0.8%
0.229
 
0.8%
114
 
0.4%
ValueCountFrequency (%)
03655
95.8%
0.229
 
0.8%
0.435
 
0.9%
0.652
 
1.4%
0.831
 
0.8%
ValueCountFrequency (%)
114
 
0.4%
0.831
0.8%
0.652
1.4%
0.435
0.9%
0.229
0.8%

Month
Categorical

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
Mar
1894 
Nov
760 
May
390 
Dec
216 
Feb
 
184
Other values (5)
372 

Length

Max length4
Median length3
Mean length3.007599581
Min length3

Characters and Unicode

Total characters11477
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFeb
2nd rowFeb
3rd rowFeb
4th rowFeb
5th rowFeb
ValueCountFrequency (%)
Mar1894
49.6%
Nov760
19.9%
May390
 
10.2%
Dec216
 
5.7%
Feb184
 
4.8%
Oct115
 
3.0%
Sep86
 
2.3%
Aug76
 
2.0%
Jul66
 
1.7%
June29
 
0.8%
2021-01-15T14:57:47.986131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T14:57:48.115191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
mar1894
49.6%
nov760
19.9%
may390
 
10.2%
dec216
 
5.7%
feb184
 
4.8%
oct115
 
3.0%
sep86
 
2.3%
aug76
 
2.0%
jul66
 
1.7%
june29
 
0.8%

Most occurring characters

ValueCountFrequency (%)
M2284
19.9%
a2284
19.9%
r1894
16.5%
N760
 
6.6%
o760
 
6.6%
v760
 
6.6%
e515
 
4.5%
y390
 
3.4%
c331
 
2.9%
D216
 
1.9%
Other values (12)1283
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7661
66.8%
Uppercase Letter3816
33.2%

Most frequent character per category

ValueCountFrequency (%)
a2284
29.8%
r1894
24.7%
o760
 
9.9%
v760
 
9.9%
e515
 
6.7%
y390
 
5.1%
c331
 
4.3%
b184
 
2.4%
u171
 
2.2%
t115
 
1.5%
Other values (4)257
 
3.4%
ValueCountFrequency (%)
M2284
59.9%
N760
 
19.9%
D216
 
5.7%
F184
 
4.8%
O115
 
3.0%
J95
 
2.5%
S86
 
2.3%
A76
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11477
100.0%

Most frequent character per script

ValueCountFrequency (%)
M2284
19.9%
a2284
19.9%
r1894
16.5%
N760
 
6.6%
o760
 
6.6%
v760
 
6.6%
e515
 
4.5%
y390
 
3.4%
c331
 
2.9%
D216
 
1.9%
Other values (12)1283
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11477
100.0%

Most frequent character per block

ValueCountFrequency (%)
M2284
19.9%
a2284
19.9%
r1894
16.5%
N760
 
6.6%
o760
 
6.6%
v760
 
6.6%
e515
 
4.5%
y390
 
3.4%
c331
 
2.9%
D216
 
1.9%
Other values (12)1283
11.2%

OperatingSystems
Real number (ℝ≥0)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.084119497
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size29.9 KiB
2021-01-15T14:57:48.218757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8536403645
Coefficient of variation (CV)0.4095928116
Kurtosis10.25073085
Mean2.084119497
Median Absolute Deviation (MAD)0
Skewness1.9584395
Sum7953
Variance0.728701872
MonotocityNot monotonic
2021-01-15T14:57:48.363851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
22191
57.4%
1784
 
20.5%
3659
 
17.3%
4159
 
4.2%
817
 
0.4%
73
 
0.1%
62
 
0.1%
51
 
< 0.1%
ValueCountFrequency (%)
1784
 
20.5%
22191
57.4%
3659
 
17.3%
4159
 
4.2%
51
 
< 0.1%
ValueCountFrequency (%)
817
 
0.4%
73
 
0.1%
62
 
0.1%
51
 
< 0.1%
4159
4.2%

Browser
Real number (ℝ≥0)

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.361373166
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Memory size29.9 KiB
2021-01-15T14:57:48.503254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.665206854
Coefficient of variation (CV)0.7051858124
Kurtosis12.60639535
Mean2.361373166
Median Absolute Deviation (MAD)0
Skewness3.15000602
Sum9011
Variance2.772913868
MonotocityNot monotonic
2021-01-15T14:57:48.653477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
22414
63.3%
1766
 
20.1%
4271
 
7.1%
5165
 
4.3%
659
 
1.5%
1047
 
1.2%
330
 
0.8%
829
 
0.8%
1316
 
0.4%
714
 
0.4%
Other values (3)5
 
0.1%
ValueCountFrequency (%)
1766
 
20.1%
22414
63.3%
330
 
0.8%
4271
 
7.1%
5165
 
4.3%
ValueCountFrequency (%)
1316
 
0.4%
123
 
0.1%
111
 
< 0.1%
1047
1.2%
91
 
< 0.1%

Region
Real number (ℝ≥0)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.038784067
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size29.9 KiB
2021-01-15T14:57:48.795726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.365670817
Coefficient of variation (CV)0.7784925698
Kurtosis-0.03949840542
Mean3.038784067
Median Absolute Deviation (MAD)1
Skewness1.039824957
Sum11596
Variance5.596398416
MonotocityNot monotonic
2021-01-15T14:57:48.950060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
11563
41.0%
3709
18.6%
2373
 
9.8%
4334
 
8.8%
6243
 
6.4%
7239
 
6.3%
9135
 
3.5%
8126
 
3.3%
594
 
2.5%
ValueCountFrequency (%)
11563
41.0%
2373
 
9.8%
3709
18.6%
4334
 
8.8%
594
 
2.5%
ValueCountFrequency (%)
9135
3.5%
8126
3.3%
7239
6.3%
6243
6.4%
594
 
2.5%

TrafficType
Real number (ℝ≥0)

Distinct18
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.580974843
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Memory size29.9 KiB
2021-01-15T14:57:49.524064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile11
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.554075016
Coefficient of variation (CV)0.9924881274
Kurtosis5.3565105
Mean3.580974843
Median Absolute Deviation (MAD)1
Skewness2.217715396
Sum13665
Variance12.63144922
MonotocityNot monotonic
2021-01-15T14:57:49.677381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
21340
35.1%
1910
23.8%
3599
15.7%
10208
 
5.5%
4190
 
5.0%
8161
 
4.2%
690
 
2.4%
1373
 
1.9%
1166
 
1.7%
561
 
1.6%
Other values (8)118
 
3.1%
ValueCountFrequency (%)
1910
23.8%
21340
35.1%
3599
15.7%
4190
 
5.0%
561
 
1.6%
ValueCountFrequency (%)
2050
1.3%
191
 
< 0.1%
161
 
< 0.1%
152
 
0.1%
144
 
0.1%

VisitorType
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
Returning_Visitor
3179 
New_Visitor
621 
Other
 
16

Length

Max length17
Median length17
Mean length15.97327044
Min length5

Characters and Unicode

Total characters60954
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReturning_Visitor
2nd rowReturning_Visitor
3rd rowReturning_Visitor
4th rowReturning_Visitor
5th rowReturning_Visitor
ValueCountFrequency (%)
Returning_Visitor3179
83.3%
New_Visitor621
 
16.3%
Other16
 
0.4%
2021-01-15T14:57:49.994853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T14:57:50.113549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
returning_visitor3179
83.3%
new_visitor621
 
16.3%
other16
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i10779
17.7%
t6995
11.5%
r6995
11.5%
n6358
10.4%
e3816
 
6.3%
_3800
 
6.2%
V3800
 
6.2%
s3800
 
6.2%
o3800
 
6.2%
R3179
 
5.2%
Other values (6)7632
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter49538
81.3%
Uppercase Letter7616
 
12.5%
Connector Punctuation3800
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
i10779
21.8%
t6995
14.1%
r6995
14.1%
n6358
12.8%
e3816
 
7.7%
s3800
 
7.7%
o3800
 
7.7%
u3179
 
6.4%
g3179
 
6.4%
w621
 
1.3%
ValueCountFrequency (%)
V3800
49.9%
R3179
41.7%
N621
 
8.2%
O16
 
0.2%
ValueCountFrequency (%)
_3800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin57154
93.8%
Common3800
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
i10779
18.9%
t6995
12.2%
r6995
12.2%
n6358
11.1%
e3816
 
6.7%
V3800
 
6.6%
s3800
 
6.6%
o3800
 
6.6%
R3179
 
5.6%
u3179
 
5.6%
Other values (5)4453
7.8%
ValueCountFrequency (%)
_3800
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII60954
100.0%

Most frequent character per block

ValueCountFrequency (%)
i10779
17.7%
t6995
11.5%
r6995
11.5%
n6358
10.4%
e3816
 
6.3%
_3800
 
6.2%
V3800
 
6.2%
s3800
 
6.2%
o3800
 
6.2%
R3179
 
5.2%
Other values (6)7632
12.5%

Weekend
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
False
2874 
True
942 
ValueCountFrequency (%)
False2874
75.3%
True942
 
24.7%
2021-01-15T14:57:50.169887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Revenue
Categorical

UNIFORM

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
1908 
1
1908 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01908
50.0%
11908
50.0%
2021-01-15T14:57:50.410212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T14:57:50.526720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
01908
50.0%
11908
50.0%

Most occurring characters

ValueCountFrequency (%)
01908
50.0%
11908
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3816
100.0%

Most frequent character per category

ValueCountFrequency (%)
01908
50.0%
11908
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common3816
100.0%

Most frequent character per script

ValueCountFrequency (%)
01908
50.0%
11908
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3816
100.0%

Most frequent character per block

ValueCountFrequency (%)
01908
50.0%
11908
50.0%

Interactions

2021-01-15T14:57:19.510778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:19.694691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:19.801803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:19.915044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:20.028177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:20.139450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:20.248321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:20.363232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:20.477451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:20.589576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:20.696861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:20.807750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:20.913987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:21.026700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:21.138147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:21.244675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:21.451888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:21.557886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:21.662692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:21.763760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:21.866075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:21.973300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:22.081851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:22.185931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:22.286791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:22.390675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:22.490104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:22.595162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:22.699464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:22.803447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:22.900908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:23.003830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:23.106558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:23.204901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:23.303712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:23.408299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:23.514574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:23.616730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:23.715971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:23.822476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:23.921212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:24.025998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:24.127994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:24.240952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:24.349418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:24.456241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:24.568743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:24.677646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:24.786753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:25.029716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:25.143995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:25.255188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:25.361953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:25.473952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:25.579810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:25.690476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:25.801147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:25.911155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:26.015970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:26.119813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:26.229620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:26.333552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:26.438182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:26.548990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:26.659830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:26.767426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:26.872612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:26.982858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:27.088009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:27.198333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:27.306466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:27.410434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:27.510321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:27.608165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:27.713289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:27.819193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:27.919171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:28.025467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:28.131763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:28.235455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:28.335130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:28.438284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:28.537213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:28.641228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:28.749863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:28.858819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:28.957741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:29.056241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:29.332625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:29.438506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:29.538469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:29.644593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:29.750597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:29.853411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:29.953582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:30.058270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:30.157622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:30.262239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:30.365795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:30.478921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:30.587285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:30.695245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:30.808301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:30.920894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:31.029313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:31.137647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:31.254578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:31.365436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:31.473378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:31.585316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:31.692667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:31.805711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:31.918556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:32.031636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:32.140293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:32.246555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:32.359086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:32.471096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:32.578776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:32.686978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:32.800812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:32.912107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:33.019817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:33.131363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:33.238627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:33.351365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:33.462573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:33.571945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:33.676420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:33.778919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:33.887861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:33.996267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:34.100796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:34.204664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:34.314684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:34.424204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:34.763326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:34.873582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:34.978364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:35.087615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:35.196303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:35.301636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:35.400739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:35.499510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:35.608738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:35.713967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:35.816678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:35.919483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:36.033614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:36.145380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:36.258551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:36.369917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:36.483288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:36.588737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:36.692656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:36.802705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:36.907910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:37.011351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:37.122288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:37.231313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:37.336648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:37.442366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:37.553646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:37.664937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:37.773262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:37.877365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:37.981086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:38.091220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:38.201077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:38.305983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:38.405691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:38.503103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:38.607651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:38.711149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:38.811080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:38.915240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:39.022037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:39.132445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:39.236133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:39.335530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:39.438215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:39.542179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:39.645455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:39.760938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:39.867746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:39.972391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:40.083951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:40.194259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:40.300699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:40.408489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:40.521772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:40.635201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:40.745573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:40.851750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:41.032079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:41.138103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:41.565176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:41.678866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:41.784497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:41.888224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:41.998443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:42.107907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:42.213974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:42.319935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:42.431325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:42.542748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:42.651964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:42.756254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:42.864313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-15T14:57:42.968624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-01-15T14:57:50.619133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-15T14:57:51.006552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-15T14:57:51.189247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-15T14:57:51.376537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-15T14:57:51.534144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-15T14:57:43.256599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-15T14:57:43.726449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

oidAdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
0100.000.010.0000000.2000000.2000000.00.0Feb1111Returning_VisitorFalse0
1200.000.0264.0000000.0000000.1000000.00.0Feb2212Returning_VisitorFalse0
230-1.00-1.01-1.0000000.2000000.2000000.00.0Feb4193Returning_VisitorFalse0
3400.000.022.6666670.0500000.1400000.00.0Feb3224Returning_VisitorFalse0
4500.000.010627.5000000.0200000.0500000.00.0Feb3314Returning_VisitorTrue0
5600.000.019154.2166670.0157890.0245610.00.0Feb2213Returning_VisitorFalse0
670-1.00-1.01-1.0000000.2000000.2000000.00.4Feb2433Returning_VisitorFalse0
781-1.00-1.01-1.0000000.2000000.2000000.00.0Feb1215Returning_VisitorTrue0
8900.000.0237.0000000.0000000.1000000.00.8Feb2223Returning_VisitorFalse0
91000.000.03738.0000000.0000000.0222220.00.4Feb2412Returning_VisitorFalse0

Last rows

oidAdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
3806380700.00000000.00000036872.8333330.0000000.00705981.0272960.0Nov2418New_VisitorFalse1
3807380812219.2743594857.1666671072640.1356970.0000000.0154906.4792060.0Nov1212Returning_VisitorTrue1
3808380900.00000000.000000241010.2500000.0083330.01666731.8196770.0Dec3282Returning_VisitorFalse1
380938106413.49961200.000000836072.0323910.0070590.0240241.2400710.0Nov3281Returning_VisitorTrue1
3810381100.00000000.0000005277.5000000.0000000.04000042.0304410.0Nov2232Returning_VisitorFalse1
381138126133.46666700.000000442664.4458330.0020410.01088497.8608360.0Nov2213Returning_VisitorTrue1
381238137139.57500000.00000030986.5000000.0000000.01142936.3928610.0Dec21012New_VisitorFalse1
3813381410.0000002211.2500001444627.4895710.0013610.0206640.0000000.0Nov2212Returning_VisitorFalse1
381438157150.35714319.00000022111431.0012400.0111490.0219041.5824730.0Nov2512Returning_VisitorTrue1
38153816316.000000386.000000152773.5000000.0000000.03000078.8117250.0Dec2212Returning_VisitorFalse1